相关论文: Emerging STEM education researchers' positioning a…
The epistemology of science students, i.e., their beliefs about the nature of the knowledge they are learning, about what they have to do to learn it, and about how they will use that knowledge, often plays a powerful role in what they…
E-learning has been continuously present in current educational discourse, thanks to technological advances, learning methodologies and public or organizational policies, among other factors. However, despite its boom and dominance in…
Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text. So far,NER still approaches entity typing as a task of classification into universal classes (e.g. date, person, or…
Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However,…
Underlying complex systems, there is a rich web of interconnected components that determine the relational properties of the system. Yet, traditional methods used in education sciences often disregard the underlying complexity of the…
We describe a course designed to help future educators build an integrated understanding of the different elements of physics education research (PER), including: research into student learning, content knowledge from the perspective of how…
Artificial intelligence (AI) is transforming elementary STEM education, yet evidence remains fragmented. This systematic review synthesizes 258 studies (2020-2025) examining AI applications across eight categories: intelligent tutoring…
Despite numerous calls for the transformation of undergraduate STEM education, there is still a lack of successful models for creating large-scale, systemic cultural changes in STEM departments. To date, change efforts have generally…
The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature…
Nowadays, engineers need to tackle many unprecedented challenges that are often complex, and, most importantly, cannot be exhaustively compartmentalized into a single engineering discipline. In other words, most engineering problems need to…
Scientometric data is used to investigate empirically the emergence of search regimes in Biotechnology, Genomics, and Nanotechnology. Complex regimes can emerge when three independent sources of variance interact. In our model, researchers…
This paper examines the integration of STEAM (Science, Technology, Engineering, Arts, and Mathematics) into education, emphasizing the inclusion of the Arts to foster creativity alongside traditional STEM skills. STEAM encourages…
Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However,…
Current concerns over reforming engineering education have focused attention on helping students develop skills and an adaptive expertise. Phenomenological guidelines for instruction along these lines can be understood as arising out of an…
Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large data sets and machine learning techniques. In…
There is an increasing worldwide demand for people educated into science and technology. Unfortunately, girls and underprivileged students are often underrepresented in Science, Technology, Engineering and Mathematics (STEM) education…
Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also…
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small amount of labelled samples. However, these approaches have…
Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the…
Research experience and mentoring has been identified as an effective intervention for increasing student engagement and retention in the STEM fields, with high impact on students from undeserved populations. However, one-on-one mentoring…